CSSS 2009 Santa Fe-Readings
From Santa Fe Institute Events Wiki
|CSSS Santa Fe 2009|
- 1 Liz Bradley: Introduction to Nonlinear Dynamics
- 2 Tom Carter
- 3 Owen Densmore and Stephen Guerin
- 4 Peter Dodds: Networks
- 5 Olaf Sporns: Neuroscience
- 6 Doug Erwin: The History of Life and the Construction of Biodiversity
- 7 Adaptive Modeling in Social Science (Nathan Collins)
- 8 Scott Pauls: Partition Decoupling for Roll Call Data
- 9 Willemien Kets: Social Systems: Complexity, Reasoning and Beliefs
- 10 David Krakauer: Evolution
- 11 Jessica Flack: Regulation
- 12 Lauren Ancel Meyers: Network and Mathematical Epidemiology
- 13 Jennifer Dunne: Ecological Network Structures
- 14 D. Eric Smith: The Origin of Life
- 15 Jenna Bednar: Game Theory and Political Science
- 16 Greg Leibon: Statistical Learning
- 17 Van Savage: Scaling and Power Laws
- 18 J. Doyne Farmer: Financial Modeling and Technology Evolution
- 19 Cris Moore: Phase Transitions
- 20 Nathan Eagle: Data! Data!
- 21 Caroline Buckee: The Spread and Diversity of Disease
- 22 Scott E. Page: Modeling in the Social Sciences
Liz Bradley: Introduction to Nonlinear Dynamics
- Numerical Solution of Differential Equations: Notes for CSCI3656
- Time Series Analysis
- Slides for Lecture 1 (2008)
- Slides for Lecture 2 (2008)
- Slides for Lecture 3 (2008)
- Slides for Lecture 4 (2008)
- Intro to Nonlinear Dynamics: Maps
- Intro to Nonlinear Dynamics: Flows
- Intro to Nonlinear Dynamics: Methods for Solving
- Intro to Nonlinear Dynamics: Application and Examples
FRACTAL Villages 
Here is a link to a page with various background readings -- I'll be talking about some of this material, watch the wiki for days/times
Owen Densmore and Stephen Guerin
For the NetLogo Tutorial, please read the first page and follow the instructions for downloading and installing the software. Then run at least one of the File > Models Library example models .. both for the fun of it and to make sure your software download works!
Peter Dodds: Networks
Please see Peter's website for readings as well as notes and supplemental material.
Peter's Complex Systems Summer School 2009 Webpage
Olaf Sporns: Neuroscience
- Mapping The Structural Core of the Human Cerebral Cortex
- Complex Brain Networks
- Mapping Information Flow in Sensorimotor Networks
Doug Erwin: The History of Life and the Construction of Biodiversity
My talks will focus on various aspects of the construction of biodiversity, with the overall theme being how much we do not yet know about this problem, despite its obvious importance. The first lecture will provide an overview of the history of life, the nature of the fossil record, and the various aspects of diversity. In the second talk we will discuss a variety of different conceptual models to understand the growth of diversity, as well as their problems. There will be a number of possibilities for projects here. The third talk will focus on the Cambrian radiation of animals (about 570-510 million years ago) and particularly on the role of changes in developmental gene regulation. Some of the issues that I raise in these lectures will be explored in more detail by others later in the week.
This paper discusses the last common bilaterian ancestor: Media:Erwin and Davidson 2002.pdf
This paper discusses the nature of change in gene regulatory networks: Media:Erwin_and_Davidson_2009.pdf
This paper provides further background on morphologic disparity: Media:Erwin_2007_disparity.pdf
Here is a powerpoint of the talk: Media:Erwin_Powerpoint.pdf
Adaptive Modeling in Social Science (Nathan Collins)
Papers you should at least have a look at prior to the lectures are marked with *.
Colin Camerer and Teck Ho, Experience-weighted attraction (EWA) learning in normal-form games," Econometrica, 67, July 1999, 827-874.
Camerer, Ho, and Chong, Function EWA: A one-parameter model of learning in games.*
Nathan Collins, Risk Learning.*
Sutton and Barto, Reinforcement Learning. An extensive introduction to reinforcement learning methods. I will cover an infinitesimal portion of this material.
Jonathan Bendor, Daniel Diermeier, and Michael Ting, A Behavioral Model of Turnout. (This is an older, working-paper version. The published version is available at jstor.org.)
Nathan Collins, Sunil Kumar, and Jonathan Bendor, The Adaptive Dynamics of Turnout, Journal of Politics 71(2), April 2009, 457-472. (You will get a hard copy of this on Monday.)*
Categorization-based models (which we may or may not get to):
Love, Medin, and Gureckis, SUSTAIN: a network model of category learning.
Collins, A Unified Model of Spatial Voting.*
Scott Pauls: Partition Decoupling for Roll Call Data
I will be discussing an application of statistical learning methods to roll call votes of the U.S. Congress. Here, I will post slides as well as supporting material.
As a point of comparison, I discuss spatial models of voting, focusing on those of Poole and Rosenthal. You can find information about this method (as well as results and tons of data) at Keith Poole's website.
In the talk, I reference a bunch of matlab code and provide copies of some of the routines. The entire PDM matlab package is here. Note: this link was broken but I've fixed it now.
Willemien Kets: Social Systems: Complexity, Reasoning and Beliefs
Many social systems are complex: they consist of a large number of interacting agents who adapt to their environment, and feature both positive and negative feedbacks. This suggests that social systems can be analyzed with similar techniques as complex systems in e.g. physics. Indeed, some models based on models for physical systems have been successful at explaining some features of social systems, thus providing new insights. In particular, these models are well-suited to investigate how behavior at the micro-scale leads to aggregate patterns of behavior. However, social systems also differ from other complex systems in important ways. A key feature of social systems that its constituents--individuals--can reason and form beliefs about the system and each other. This means that individuals can interact strategically, i.e., they behave in a way that they believe will benefit them most, given others' behavior. It also means that individuals---even the fully rational individual assumed in game theory and economics---can be wrong in their beliefs. This may give rise to types of behavior that are not present in physical and other complex systems. In the first talk, I discuss a model to analyze a simple game called the minority game that builds on insights from physics and illustrate how such models can shed light on different types of macro-scale behavior and how they may even teach us something about the emergence of heuristics. In the second talk, I take a closer look at the relevant types of micro-behavior in social systems. Only if we have a thorough understanding of the behavior of individuals at micro-scale can we investigate how this microbehavior influences aggregate performance of social systems. I introduce a formal framework to describe individuals' reasoning processes and beliefs. This allows us to explore the conditions under which reasoning processes and belief formation have a large impact on behavior. It also allows us to analyze the implications of bounded rationality in a systematic manner. Ultimately, the goal is to use this detailed understanding of micro-behavior to understand the aggregate behavior of social systems.
- Kets, W. (2007), The minority game: An economics perspective
- Brandenburger, A. (1999), The power of paradox: Some recent developments in interactive epistemology, International Journal of Game Theory
David Krakauer: Evolution
Jessica Flack: Regulation
Please review the following papers before lectures on Monday and Tuesday.
- Robustness and Complexity in Animal Communication
- Error and Attack Tolerance of Complex Networks
- Policing and Niche Construction
- Policing and Niche Construction Supplementary
- Backup without Redundency
Lauren Ancel Meyers: Network and Mathematical Epidemiology
Jennifer Dunne: Ecological Network Structures
D. Eric Smith: The Origin of Life
Jenna Bednar: Game Theory and Political Science
Greg Leibon: Statistical Learning
Van Savage: Scaling and Power Laws
- Power Laws, Fractals and the Structure and Dynamics of Vascular Networks
- Using Biological Scaling to Understand Tumor Growth Dynamics and Sleep Times